Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
# data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7f1559fd9208>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7f1559ed2710>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.1.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    
    input_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='input_real')
    input_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')
    learn_rate = tf.placeholder(tf.float32)
    return input_real, input_z, learn_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
def leaky_relu(x, alpha=0.05, name='leaky_relu'):
    return tf.maximum(x, alpha*x, name=name)
In [ ]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    
    
    with tf.variable_scope('discriminator', reuse=reuse):
        
        # 28x28x1
        x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer())
        relu1 = leaky_relu(x1)
        
        x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer())
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = leaky_relu(bn2)
        
        x3 = tf.layers.conv2d(relu2, 256, 5, strides=2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer())
        bn3 = tf.layers.batch_normalization(x3, training=True)
        relu3 = leaky_relu(bn3)
        
#         x4 = tf.layers.conv2d(relu2, 512, 5, strides=2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer())
#         bn4 = tf.layers.batch_normalization(x4, training=True)
#         relu4 = leaky_relu(bn4)


        flat = tf.contrib.layers.flatten(relu3)
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
        return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [16]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    
    
    
    
    with tf.variable_scope('generator', reuse=not is_train):
        x1 = tf.layers.dense(z, 4*4*1024)
        
        x2 = tf.reshape(x1, (-1, 4, 4, 1024))
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = leaky_relu(x2)

        x3 = tf.layers.conv2d_transpose(x2, 512, 4, strides=1, padding='valid', kernel_initializer=tf.contrib.layers.xavier_initializer())
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = leaky_relu(x3)
        
        x4 = tf.layers.conv2d_transpose(x3, 256, 5, strides=2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer())
        x4 = tf.layers.batch_normalization(x4, training=is_train)
        x4 = leaky_relu(x4)
        
        x5 = tf.layers.conv2d_transpose(x4, 128, 5, strides=1, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer())
        x5 = tf.layers.batch_normalization(x5, training=is_train)
        x5 = leaky_relu(x5)
        

        
        logits = tf.layers.conv2d_transpose(x5, out_channel_dim, 5, strides=2, padding='same', kernel_initializer=tf.contrib.layers.xavier_initializer())
    
        out = tf.tanh(logits)
    return out



"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [9]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    g_model = generator(input_z, out_channel_dim, is_train=True)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, True)
    
    smooth = 0.1
    
    d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)*(1.0-smooth)))
    d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))
    
    d_loss = d_loss_real + d_loss_fake
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [10]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate=learning_rate, beta1=beta1).minimize(d_loss, var_list = d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate=learning_rate, beta1=beta1).minimize(g_loss, var_list = g_vars)
    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [11]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [18]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    input_real, input_z, learn_rate = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
    d_loss, g_loss = model_loss(input_real, input_z, data_shape[3])
    d_opt, g_opt = model_opt(d_loss, g_loss, learn_rate, beta1)
    
    steps = 0
    
    
    samples = []
    losses = []
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                steps += 1
                batch_images = batch_images*2
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                
                _ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z, learn_rate: learning_rate})
                _ = sess.run(g_opt, feed_dict={input_z:batch_z, input_real:batch_images, learn_rate: learning_rate} )
                _ = sess.run(g_opt, feed_dict={input_z:batch_z, input_real:batch_images, learn_rate: learning_rate} )
#                 _ = sess.run(g_opt, feed_dict={input_z:batch_z, input_real:batch_images, learn_rate: learning_rate} )

                if steps % 20 ==0:
                    train_loss_d = d_loss.eval({input_real:batch_images, input_z:batch_z})
                    train_loss_g = g_loss.eval({input_z: batch_z})
                    
                    print("Epoch {}/{}...".format(epoch_i+1, epoch_count),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))

                    losses.append((train_loss_d, train_loss_g))
                    
                if steps % 100 == 0:
                    
                    show_generator_output(sess, 25, input_z, data_shape[3], data_image_mode)
    

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [ ]:
batch_size = 32
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5 #this was 0.3


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Discriminator Loss: 1.4006... Generator Loss: 1.9130
Epoch 1/2... Discriminator Loss: 0.7685... Generator Loss: 1.8494
Epoch 1/2... Discriminator Loss: 1.5658... Generator Loss: 3.0015
Epoch 1/2... Discriminator Loss: 2.7182... Generator Loss: 0.2184
Epoch 1/2... Discriminator Loss: 1.5068... Generator Loss: 0.5725
Epoch 1/2... Discriminator Loss: 2.0334... Generator Loss: 0.5384
Epoch 1/2... Discriminator Loss: 1.4410... Generator Loss: 0.8717
Epoch 1/2... Discriminator Loss: 1.9199... Generator Loss: 0.5003
Epoch 1/2... Discriminator Loss: 2.3831... Generator Loss: 0.4777
Epoch 1/2... Discriminator Loss: 1.5775... Generator Loss: 0.8710
Epoch 1/2... Discriminator Loss: 1.5630... Generator Loss: 0.6119
Epoch 1/2... Discriminator Loss: 1.5801... Generator Loss: 0.6624
Epoch 1/2... Discriminator Loss: 1.3573... Generator Loss: 0.8482
Epoch 1/2... Discriminator Loss: 1.4981... Generator Loss: 0.6796
Epoch 1/2... Discriminator Loss: 1.5598... Generator Loss: 0.4985
Epoch 1/2... Discriminator Loss: 1.5777... Generator Loss: 0.6158
Epoch 1/2... Discriminator Loss: 1.4926... Generator Loss: 0.6315
Epoch 1/2... Discriminator Loss: 1.5768... Generator Loss: 0.4220
Epoch 1/2... Discriminator Loss: 1.6199... Generator Loss: 0.8234
Epoch 1/2... Discriminator Loss: 2.0747... Generator Loss: 0.2237
Epoch 1/2... Discriminator Loss: 1.7570... Generator Loss: 0.5099
Epoch 1/2... Discriminator Loss: 1.5138... Generator Loss: 0.7122
Epoch 1/2... Discriminator Loss: 1.6015... Generator Loss: 0.6402
Epoch 1/2... Discriminator Loss: 1.8481... Generator Loss: 0.3436
Epoch 1/2... Discriminator Loss: 1.4915... Generator Loss: 0.6440
Epoch 1/2... Discriminator Loss: 1.6880... Generator Loss: 0.6320
Epoch 1/2... Discriminator Loss: 1.4129... Generator Loss: 0.9198
Epoch 1/2... Discriminator Loss: 1.5580... Generator Loss: 0.8174
Epoch 1/2... Discriminator Loss: 1.6863... Generator Loss: 0.3817
Epoch 1/2... Discriminator Loss: 1.5642... Generator Loss: 0.7420
Epoch 1/2... Discriminator Loss: 1.5872... Generator Loss: 0.5060
Epoch 1/2... Discriminator Loss: 1.5062... Generator Loss: 0.8299
Epoch 1/2... Discriminator Loss: 1.5320... Generator Loss: 0.6378
Epoch 1/2... Discriminator Loss: 1.5567... Generator Loss: 0.7876
Epoch 1/2... Discriminator Loss: 1.5294... Generator Loss: 0.5423
Epoch 1/2... Discriminator Loss: 1.6437... Generator Loss: 0.3938
Epoch 1/2... Discriminator Loss: 1.7289... Generator Loss: 0.8999
Epoch 1/2... Discriminator Loss: 1.6329... Generator Loss: 0.4119
Epoch 1/2... Discriminator Loss: 1.5826... Generator Loss: 0.6207
Epoch 1/2... Discriminator Loss: 1.5530... Generator Loss: 0.6187
Epoch 1/2... Discriminator Loss: 1.6771... Generator Loss: 0.3600
Epoch 1/2... Discriminator Loss: 1.6450... Generator Loss: 0.4533
Epoch 1/2... Discriminator Loss: 1.6924... Generator Loss: 0.4766
Epoch 1/2... Discriminator Loss: 1.5471... Generator Loss: 0.6128
Epoch 1/2... Discriminator Loss: 1.5857... Generator Loss: 0.4318
Epoch 1/2... Discriminator Loss: 1.5062... Generator Loss: 0.4646
Epoch 1/2... Discriminator Loss: 1.5327... Generator Loss: 0.9504
Epoch 1/2... Discriminator Loss: 1.5263... Generator Loss: 0.5265
Epoch 1/2... Discriminator Loss: 1.4797... Generator Loss: 0.8929
Epoch 1/2... Discriminator Loss: 1.6952... Generator Loss: 0.3696
Epoch 1/2... Discriminator Loss: 1.4948... Generator Loss: 0.5217
Epoch 1/2... Discriminator Loss: 1.4200... Generator Loss: 0.9117
Epoch 1/2... Discriminator Loss: 1.3986... Generator Loss: 0.7921
Epoch 1/2... Discriminator Loss: 1.5073... Generator Loss: 0.5744
Epoch 1/2... Discriminator Loss: 1.6446... Generator Loss: 0.4504
Epoch 1/2... Discriminator Loss: 1.5436... Generator Loss: 0.4808
Epoch 1/2... Discriminator Loss: 1.4003... Generator Loss: 0.5748
Epoch 1/2... Discriminator Loss: 1.4945... Generator Loss: 0.6078
Epoch 1/2... Discriminator Loss: 1.4621... Generator Loss: 0.9053
Epoch 1/2... Discriminator Loss: 1.8392... Generator Loss: 0.2802
Epoch 1/2... Discriminator Loss: 1.5407... Generator Loss: 0.6307
Epoch 1/2... Discriminator Loss: 1.4466... Generator Loss: 0.9482
Epoch 1/2... Discriminator Loss: 1.6910... Generator Loss: 0.3557
Epoch 1/2... Discriminator Loss: 1.4086... Generator Loss: 0.5825
Epoch 1/2... Discriminator Loss: 1.3652... Generator Loss: 0.6538
Epoch 1/2... Discriminator Loss: 1.4909... Generator Loss: 1.0319
Epoch 1/2... Discriminator Loss: 1.5230... Generator Loss: 0.4948
Epoch 1/2... Discriminator Loss: 1.5681... Generator Loss: 0.4590
Epoch 1/2... Discriminator Loss: 1.3902... Generator Loss: 0.7101
Epoch 1/2... Discriminator Loss: 1.6129... Generator Loss: 1.1906
Epoch 1/2... Discriminator Loss: 1.3851... Generator Loss: 0.6214
Epoch 1/2... Discriminator Loss: 1.4851... Generator Loss: 0.5678
Epoch 1/2... Discriminator Loss: 1.5764... Generator Loss: 0.4014
Epoch 1/2... Discriminator Loss: 1.3669... Generator Loss: 0.7413
Epoch 1/2... Discriminator Loss: 1.3821... Generator Loss: 0.6435
Epoch 1/2... Discriminator Loss: 1.4179... Generator Loss: 0.5634
Epoch 1/2... Discriminator Loss: 1.7333... Generator Loss: 0.3484
Epoch 1/2... Discriminator Loss: 1.8057... Generator Loss: 0.3073
Epoch 1/2... Discriminator Loss: 1.4515... Generator Loss: 0.9829
Epoch 1/2... Discriminator Loss: 1.4717... Generator Loss: 0.6823
Epoch 1/2... Discriminator Loss: 1.4608... Generator Loss: 0.5356
Epoch 1/2... Discriminator Loss: 1.4135... Generator Loss: 0.5621
Epoch 1/2... Discriminator Loss: 1.3782... Generator Loss: 0.8476
Epoch 1/2... Discriminator Loss: 1.3549... Generator Loss: 0.6863
Epoch 1/2... Discriminator Loss: 1.5421... Generator Loss: 0.6418
Epoch 1/2... Discriminator Loss: 1.4887... Generator Loss: 0.6144
Epoch 1/2... Discriminator Loss: 1.7881... Generator Loss: 0.2952
Epoch 1/2... Discriminator Loss: 1.5144... Generator Loss: 0.4555
Epoch 1/2... Discriminator Loss: 1.4806... Generator Loss: 0.5807
Epoch 1/2... Discriminator Loss: 1.7232... Generator Loss: 0.3450
Epoch 1/2... Discriminator Loss: 1.8375... Generator Loss: 0.3172
Epoch 1/2... Discriminator Loss: 1.5376... Generator Loss: 0.4537
Epoch 1/2... Discriminator Loss: 1.4790... Generator Loss: 0.5268
Epoch 2/2... Discriminator Loss: 1.3472... Generator Loss: 0.8909
Epoch 2/2... Discriminator Loss: 1.3847... Generator Loss: 0.6794
Epoch 2/2... Discriminator Loss: 1.4841... Generator Loss: 0.5518
Epoch 2/2... Discriminator Loss: 1.2239... Generator Loss: 0.8029
Epoch 2/2... Discriminator Loss: 1.2394... Generator Loss: 0.9158
Epoch 2/2... Discriminator Loss: 1.2741... Generator Loss: 0.8528
Epoch 2/2... Discriminator Loss: 1.4746... Generator Loss: 0.5652
Epoch 2/2... Discriminator Loss: 1.4495... Generator Loss: 0.6311
Epoch 2/2... Discriminator Loss: 1.3899... Generator Loss: 0.6200
Epoch 2/2... Discriminator Loss: 1.6805... Generator Loss: 0.3886
Epoch 2/2... Discriminator Loss: 1.4944... Generator Loss: 0.5493
Epoch 2/2... Discriminator Loss: 1.5714... Generator Loss: 0.5347
Epoch 2/2... Discriminator Loss: 1.5536... Generator Loss: 0.4423
Epoch 2/2... Discriminator Loss: 1.3436... Generator Loss: 0.6783
Epoch 2/2... Discriminator Loss: 1.5912... Generator Loss: 0.4015
Epoch 2/2... Discriminator Loss: 1.3921... Generator Loss: 0.8326
Epoch 2/2... Discriminator Loss: 1.3632... Generator Loss: 0.6192
Epoch 2/2... Discriminator Loss: 1.4076... Generator Loss: 0.5508
Epoch 2/2... Discriminator Loss: 1.3785... Generator Loss: 0.5946
Epoch 2/2... Discriminator Loss: 1.4353... Generator Loss: 0.5239
Epoch 2/2... Discriminator Loss: 1.3124... Generator Loss: 0.7002
Epoch 2/2... Discriminator Loss: 1.6919... Generator Loss: 0.3724
Epoch 2/2... Discriminator Loss: 1.3125... Generator Loss: 0.6394
Epoch 2/2... Discriminator Loss: 1.2898... Generator Loss: 0.6262
Epoch 2/2... Discriminator Loss: 1.5551... Generator Loss: 0.6076
Epoch 2/2... Discriminator Loss: 1.2363... Generator Loss: 0.7592
Epoch 2/2... Discriminator Loss: 1.5637... Generator Loss: 0.3769
Epoch 2/2... Discriminator Loss: 1.4260... Generator Loss: 0.9325
Epoch 2/2... Discriminator Loss: 1.5989... Generator Loss: 0.4272
Epoch 2/2... Discriminator Loss: 1.4971... Generator Loss: 0.4685
Epoch 2/2... Discriminator Loss: 1.3680... Generator Loss: 0.6760
Epoch 2/2... Discriminator Loss: 1.5292... Generator Loss: 0.9739
Epoch 2/2... Discriminator Loss: 1.2531... Generator Loss: 0.7366
Epoch 2/2... Discriminator Loss: 1.3581... Generator Loss: 0.6361
Epoch 2/2... Discriminator Loss: 1.3441... Generator Loss: 1.0257
Epoch 2/2... Discriminator Loss: 1.4605... Generator Loss: 0.5608
Epoch 2/2... Discriminator Loss: 1.2549... Generator Loss: 0.7896
Epoch 2/2... Discriminator Loss: 1.4941... Generator Loss: 0.4764
Epoch 2/2... Discriminator Loss: 1.3529... Generator Loss: 0.8872
Epoch 2/2... Discriminator Loss: 1.5198... Generator Loss: 0.3991
Epoch 2/2... Discriminator Loss: 1.2729... Generator Loss: 0.6850
Epoch 2/2... Discriminator Loss: 1.4403... Generator Loss: 1.5096
Epoch 2/2... Discriminator Loss: 1.4735... Generator Loss: 1.0405
Epoch 2/2... Discriminator Loss: 1.6657... Generator Loss: 0.6940
Epoch 2/2... Discriminator Loss: 1.2568... Generator Loss: 0.7144
Epoch 2/2... Discriminator Loss: 1.3195... Generator Loss: 0.5461
Epoch 2/2... Discriminator Loss: 1.6420... Generator Loss: 0.3589
Epoch 2/2... Discriminator Loss: 1.1367... Generator Loss: 0.9492
Epoch 2/2... Discriminator Loss: 1.6064... Generator Loss: 0.3647
Epoch 2/2... Discriminator Loss: 1.4170... Generator Loss: 0.7035
Epoch 2/2... Discriminator Loss: 1.3878... Generator Loss: 0.5667
Epoch 2/2... Discriminator Loss: 1.3846... Generator Loss: 0.6584
Epoch 2/2... Discriminator Loss: 1.4135... Generator Loss: 0.5218
Epoch 2/2... Discriminator Loss: 1.3536... Generator Loss: 0.6017
Epoch 2/2... Discriminator Loss: 1.5581... Generator Loss: 0.4053
Epoch 2/2... Discriminator Loss: 1.3730... Generator Loss: 0.6482
Epoch 2/2... Discriminator Loss: 1.3160... Generator Loss: 0.8160
Epoch 2/2... Discriminator Loss: 1.4746... Generator Loss: 0.4750
Epoch 2/2... Discriminator Loss: 1.1299... Generator Loss: 0.8297
Epoch 2/2... Discriminator Loss: 1.2588... Generator Loss: 1.0214
Epoch 2/2... Discriminator Loss: 1.3971... Generator Loss: 0.5882
Epoch 2/2... Discriminator Loss: 1.2206... Generator Loss: 0.7227
Epoch 2/2... Discriminator Loss: 1.4362... Generator Loss: 0.4960
Epoch 2/2... Discriminator Loss: 1.2776... Generator Loss: 0.7204
Epoch 2/2... Discriminator Loss: 1.5030... Generator Loss: 0.4403
Epoch 2/2... Discriminator Loss: 1.3835... Generator Loss: 0.5252
Epoch 2/2... Discriminator Loss: 1.3897... Generator Loss: 0.6392
Epoch 2/2... Discriminator Loss: 1.4679... Generator Loss: 0.6126
Epoch 2/2... Discriminator Loss: 1.6199... Generator Loss: 0.4216
Epoch 2/2... Discriminator Loss: 1.4917... Generator Loss: 1.0156
Epoch 2/2... Discriminator Loss: 1.3801... Generator Loss: 0.6717
Epoch 2/2... Discriminator Loss: 1.4089... Generator Loss: 0.9514
Epoch 2/2... Discriminator Loss: 1.4012... Generator Loss: 0.8262
Epoch 2/2... Discriminator Loss: 1.5340... Generator Loss: 0.9101
Epoch 2/2... Discriminator Loss: 1.4355... Generator Loss: 0.6983
Epoch 2/2... Discriminator Loss: 1.4711... Generator Loss: 0.7385
Epoch 2/2... Discriminator Loss: 1.2700... Generator Loss: 0.8422
Epoch 2/2... Discriminator Loss: 1.2534... Generator Loss: 0.7610
Epoch 2/2... Discriminator Loss: 1.3141... Generator Loss: 0.7138
Epoch 2/2... Discriminator Loss: 1.4507... Generator Loss: 0.4657
Epoch 2/2... Discriminator Loss: 1.3123... Generator Loss: 0.6717
Epoch 2/2... Discriminator Loss: 1.2882... Generator Loss: 0.8866
Epoch 2/2... Discriminator Loss: 1.4517... Generator Loss: 0.5802
Epoch 2/2... Discriminator Loss: 1.3385... Generator Loss: 0.6346
Epoch 2/2... Discriminator Loss: 1.4681... Generator Loss: 0.7927
Epoch 2/2... Discriminator Loss: 1.4038... Generator Loss: 0.6319
Epoch 2/2... Discriminator Loss: 1.5027... Generator Loss: 1.2878
Epoch 2/2... Discriminator Loss: 1.4595... Generator Loss: 0.5285
Epoch 2/2... Discriminator Loss: 1.5409... Generator Loss: 0.5350
Epoch 2/2... Discriminator Loss: 1.2940... Generator Loss: 0.8617
Epoch 2/2... Discriminator Loss: 1.4674... Generator Loss: 0.4766
Epoch 2/2... Discriminator Loss: 1.4547... Generator Loss: 0.4966
Epoch 2/2... Discriminator Loss: 1.6276... Generator Loss: 0.3658
Epoch 2/2... Discriminator Loss: 1.6363... Generator Loss: 0.4114

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [ ]:
batch_size = 32
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5 #this was 0.3

"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/2... Discriminator Loss: 4.1291... Generator Loss: 0.4932
Epoch 1/2... Discriminator Loss: 3.7386... Generator Loss: 0.3953
Epoch 1/2... Discriminator Loss: 1.2681... Generator Loss: 1.1046
Epoch 1/2... Discriminator Loss: 1.3686... Generator Loss: 1.4684
Epoch 1/2... Discriminator Loss: 0.8015... Generator Loss: 1.6833
Epoch 1/2... Discriminator Loss: 0.9221... Generator Loss: 0.9647
Epoch 1/2... Discriminator Loss: 0.5959... Generator Loss: 2.2870
Epoch 1/2... Discriminator Loss: 0.4688... Generator Loss: 2.9265
Epoch 1/2... Discriminator Loss: 0.5914... Generator Loss: 2.3283
Epoch 1/2... Discriminator Loss: 0.4807... Generator Loss: 2.5257
Epoch 1/2... Discriminator Loss: 0.5034... Generator Loss: 2.2722
Epoch 1/2... Discriminator Loss: 0.4763... Generator Loss: 2.4601
Epoch 1/2... Discriminator Loss: 1.8807... Generator Loss: 0.5530
Epoch 1/2... Discriminator Loss: 1.3025... Generator Loss: 0.7942
Epoch 1/2... Discriminator Loss: 1.6229... Generator Loss: 0.8104
Epoch 1/2... Discriminator Loss: 1.2240... Generator Loss: 0.8361
Epoch 1/2... Discriminator Loss: 1.9897... Generator Loss: 0.5838
Epoch 1/2... Discriminator Loss: 1.2729... Generator Loss: 0.9011
Epoch 1/2... Discriminator Loss: 1.8080... Generator Loss: 0.6433
Epoch 1/2... Discriminator Loss: 1.7810... Generator Loss: 0.6410
Epoch 1/2... Discriminator Loss: 1.6651... Generator Loss: 0.5586
Epoch 1/2... Discriminator Loss: 1.3797... Generator Loss: 0.8634
Epoch 1/2... Discriminator Loss: 1.6515... Generator Loss: 0.5812
Epoch 1/2... Discriminator Loss: 1.5382... Generator Loss: 0.5957
Epoch 1/2... Discriminator Loss: 1.4716... Generator Loss: 0.6615
Epoch 1/2... Discriminator Loss: 1.5641... Generator Loss: 0.7534
Epoch 1/2... Discriminator Loss: 1.4641... Generator Loss: 0.7796
Epoch 1/2... Discriminator Loss: 1.4893... Generator Loss: 0.7177
Epoch 1/2... Discriminator Loss: 1.5431... Generator Loss: 0.7499
Epoch 1/2... Discriminator Loss: 1.4931... Generator Loss: 0.7788
Epoch 1/2... Discriminator Loss: 1.6552... Generator Loss: 0.6184
Epoch 1/2... Discriminator Loss: 1.5152... Generator Loss: 0.6865
Epoch 1/2... Discriminator Loss: 1.6033... Generator Loss: 0.6997
Epoch 1/2... Discriminator Loss: 1.5888... Generator Loss: 0.5982
Epoch 1/2... Discriminator Loss: 1.5792... Generator Loss: 0.6207
Epoch 1/2... Discriminator Loss: 1.7296... Generator Loss: 0.6864
Epoch 1/2... Discriminator Loss: 1.4607... Generator Loss: 0.7352
Epoch 1/2... Discriminator Loss: 1.4785... Generator Loss: 0.8174
Epoch 1/2... Discriminator Loss: 1.4464... Generator Loss: 0.7325
Epoch 1/2... Discriminator Loss: 1.5154... Generator Loss: 0.6569
Epoch 1/2... Discriminator Loss: 1.6158... Generator Loss: 0.6518
Epoch 1/2... Discriminator Loss: 1.5008... Generator Loss: 0.6509
Epoch 1/2... Discriminator Loss: 1.4206... Generator Loss: 0.7038
Epoch 1/2... Discriminator Loss: 1.5319... Generator Loss: 0.6935
Epoch 1/2... Discriminator Loss: 1.6339... Generator Loss: 0.7907
Epoch 1/2... Discriminator Loss: 1.5082... Generator Loss: 0.7438
Epoch 1/2... Discriminator Loss: 1.4383... Generator Loss: 0.7764
Epoch 1/2... Discriminator Loss: 1.5050... Generator Loss: 0.7777
Epoch 1/2... Discriminator Loss: 1.4878... Generator Loss: 0.7688
Epoch 1/2... Discriminator Loss: 1.5307... Generator Loss: 0.6784
Epoch 1/2... Discriminator Loss: 1.4555... Generator Loss: 0.7436
Epoch 1/2... Discriminator Loss: 1.4403... Generator Loss: 0.7811
Epoch 1/2... Discriminator Loss: 1.3931... Generator Loss: 0.7073
Epoch 1/2... Discriminator Loss: 1.4708... Generator Loss: 0.6740
Epoch 1/2... Discriminator Loss: 1.4854... Generator Loss: 0.6577
Epoch 1/2... Discriminator Loss: 1.6020... Generator Loss: 0.6601
Epoch 1/2... Discriminator Loss: 1.4118... Generator Loss: 0.7849
Epoch 1/2... Discriminator Loss: 1.4755... Generator Loss: 0.7176
Epoch 1/2... Discriminator Loss: 1.5437... Generator Loss: 0.6841
Epoch 1/2... Discriminator Loss: 1.4720... Generator Loss: 0.7148
Epoch 1/2... Discriminator Loss: 1.5119... Generator Loss: 0.7164
Epoch 1/2... Discriminator Loss: 1.5555... Generator Loss: 0.7134
Epoch 1/2... Discriminator Loss: 1.4609... Generator Loss: 0.6917
Epoch 1/2... Discriminator Loss: 1.5133... Generator Loss: 0.6729
Epoch 1/2... Discriminator Loss: 1.4830... Generator Loss: 0.6215
Epoch 1/2... Discriminator Loss: 1.4136... Generator Loss: 0.7671
Epoch 1/2... Discriminator Loss: 1.4136... Generator Loss: 0.7807
Epoch 1/2... Discriminator Loss: 1.4654... Generator Loss: 0.8074
Epoch 1/2... Discriminator Loss: 1.4831... Generator Loss: 0.6788
Epoch 1/2... Discriminator Loss: 1.4544... Generator Loss: 0.7244
Epoch 1/2... Discriminator Loss: 1.5111... Generator Loss: 0.7828
Epoch 1/2... Discriminator Loss: 1.5195... Generator Loss: 0.6845
Epoch 1/2... Discriminator Loss: 1.4707... Generator Loss: 0.7303
Epoch 1/2... Discriminator Loss: 1.4599... Generator Loss: 0.6885
Epoch 1/2... Discriminator Loss: 1.5583... Generator Loss: 0.6262
Epoch 1/2... Discriminator Loss: 1.4949... Generator Loss: 0.7572
Epoch 1/2... Discriminator Loss: 1.3900... Generator Loss: 0.7051
Epoch 1/2... Discriminator Loss: 1.5397... Generator Loss: 0.6575
Epoch 1/2... Discriminator Loss: 1.4931... Generator Loss: 0.7288
Epoch 1/2... Discriminator Loss: 1.3748... Generator Loss: 0.8218
Epoch 1/2... Discriminator Loss: 1.4971... Generator Loss: 0.7181
Epoch 1/2... Discriminator Loss: 1.4303... Generator Loss: 0.7836
Epoch 1/2... Discriminator Loss: 1.4167... Generator Loss: 0.7946
Epoch 1/2... Discriminator Loss: 1.4420... Generator Loss: 0.6983
Epoch 1/2... Discriminator Loss: 1.4328... Generator Loss: 0.8301
Epoch 1/2... Discriminator Loss: 1.4571... Generator Loss: 0.6894
Epoch 1/2... Discriminator Loss: 1.4746... Generator Loss: 0.7183
Epoch 1/2... Discriminator Loss: 1.4982... Generator Loss: 0.7628
Epoch 1/2... Discriminator Loss: 1.4608... Generator Loss: 0.8270
Epoch 1/2... Discriminator Loss: 1.4314... Generator Loss: 0.7601
Epoch 1/2... Discriminator Loss: 1.4602... Generator Loss: 0.6744
Epoch 1/2... Discriminator Loss: 1.4137... Generator Loss: 0.8034
Epoch 1/2... Discriminator Loss: 1.5097... Generator Loss: 0.6661
Epoch 1/2... Discriminator Loss: 1.4026... Generator Loss: 0.7416
Epoch 1/2... Discriminator Loss: 1.3950... Generator Loss: 0.8146
Epoch 1/2... Discriminator Loss: 1.4815... Generator Loss: 0.7793
Epoch 1/2... Discriminator Loss: 1.4076... Generator Loss: 0.7950
Epoch 1/2... Discriminator Loss: 1.4256... Generator Loss: 0.7764
Epoch 1/2... Discriminator Loss: 1.4509... Generator Loss: 0.7515
Epoch 1/2... Discriminator Loss: 1.4395... Generator Loss: 0.7276
Epoch 1/2... Discriminator Loss: 1.5317... Generator Loss: 0.6815
Epoch 1/2... Discriminator Loss: 1.5806... Generator Loss: 0.6716
Epoch 1/2... Discriminator Loss: 1.4559... Generator Loss: 0.8364
Epoch 1/2... Discriminator Loss: 1.4266... Generator Loss: 0.6757
Epoch 1/2... Discriminator Loss: 1.4337... Generator Loss: 0.7262
Epoch 1/2... Discriminator Loss: 1.4666... Generator Loss: 0.6777
Epoch 1/2... Discriminator Loss: 1.4715... Generator Loss: 0.7064
Epoch 1/2... Discriminator Loss: 1.4590... Generator Loss: 0.6884
Epoch 1/2... Discriminator Loss: 1.6233... Generator Loss: 0.7338
Epoch 1/2... Discriminator Loss: 1.4233... Generator Loss: 0.7668
Epoch 1/2... Discriminator Loss: 1.4681... Generator Loss: 0.7258
Epoch 1/2... Discriminator Loss: 1.4276... Generator Loss: 0.7457
Epoch 1/2... Discriminator Loss: 1.4343... Generator Loss: 0.7246
Epoch 1/2... Discriminator Loss: 1.4000... Generator Loss: 0.8298
Epoch 1/2... Discriminator Loss: 1.5078... Generator Loss: 0.7342
Epoch 1/2... Discriminator Loss: 1.3960... Generator Loss: 0.8327
Epoch 1/2... Discriminator Loss: 1.3995... Generator Loss: 0.7923
Epoch 1/2... Discriminator Loss: 1.4408... Generator Loss: 0.7150
Epoch 1/2... Discriminator Loss: 1.5252... Generator Loss: 0.6775
Epoch 1/2... Discriminator Loss: 1.3993... Generator Loss: 0.7612
Epoch 1/2... Discriminator Loss: 1.5110... Generator Loss: 0.6536
Epoch 1/2... Discriminator Loss: 1.4421... Generator Loss: 0.7652
Epoch 1/2... Discriminator Loss: 1.4187... Generator Loss: 0.8538
Epoch 1/2... Discriminator Loss: 1.4022... Generator Loss: 0.7748
Epoch 1/2... Discriminator Loss: 1.5465... Generator Loss: 0.7027
Epoch 1/2... Discriminator Loss: 1.3740... Generator Loss: 0.8163
Epoch 1/2... Discriminator Loss: 1.4210... Generator Loss: 0.7515
Epoch 1/2... Discriminator Loss: 1.4629... Generator Loss: 0.8123
Epoch 1/2... Discriminator Loss: 1.4250... Generator Loss: 0.8133
Epoch 1/2... Discriminator Loss: 1.4558... Generator Loss: 0.7083
Epoch 1/2... Discriminator Loss: 1.4318... Generator Loss: 0.7792
Epoch 1/2... Discriminator Loss: 1.4224... Generator Loss: 0.7560
Epoch 1/2... Discriminator Loss: 1.4674... Generator Loss: 0.8116
Epoch 1/2... Discriminator Loss: 1.4347... Generator Loss: 0.7721
Epoch 1/2... Discriminator Loss: 1.3857... Generator Loss: 0.8215
Epoch 1/2... Discriminator Loss: 1.3954... Generator Loss: 0.7659
Epoch 1/2... Discriminator Loss: 1.4536... Generator Loss: 0.7128
Epoch 1/2... Discriminator Loss: 1.4497... Generator Loss: 0.7564
Epoch 1/2... Discriminator Loss: 1.4084... Generator Loss: 0.7893
Epoch 1/2... Discriminator Loss: 1.4608... Generator Loss: 0.7821
Epoch 1/2... Discriminator Loss: 1.4322... Generator Loss: 0.7168
Epoch 1/2... Discriminator Loss: 1.4446... Generator Loss: 0.7809
Epoch 1/2... Discriminator Loss: 1.4399... Generator Loss: 0.7160
Epoch 1/2... Discriminator Loss: 1.4442... Generator Loss: 0.6901
Epoch 1/2... Discriminator Loss: 1.4144... Generator Loss: 0.7681
Epoch 1/2... Discriminator Loss: 1.3968... Generator Loss: 0.7834
Epoch 1/2... Discriminator Loss: 1.4664... Generator Loss: 0.7930
Epoch 1/2... Discriminator Loss: 1.4438... Generator Loss: 0.6821
Epoch 1/2... Discriminator Loss: 1.4269... Generator Loss: 0.7273
Epoch 1/2... Discriminator Loss: 1.4181... Generator Loss: 0.7836
Epoch 1/2... Discriminator Loss: 1.4169... Generator Loss: 0.7079
Epoch 1/2... Discriminator Loss: 1.4473... Generator Loss: 0.7052
Epoch 1/2... Discriminator Loss: 1.4224... Generator Loss: 0.7478
Epoch 1/2... Discriminator Loss: 1.3967... Generator Loss: 0.7751
Epoch 1/2... Discriminator Loss: 1.4197... Generator Loss: 0.8017
Epoch 1/2... Discriminator Loss: 1.4503... Generator Loss: 0.6801
Epoch 1/2... Discriminator Loss: 1.3987... Generator Loss: 0.7830
Epoch 1/2... Discriminator Loss: 1.4641... Generator Loss: 0.7940
Epoch 1/2... Discriminator Loss: 1.4245... Generator Loss: 0.7651
Epoch 1/2... Discriminator Loss: 1.4669... Generator Loss: 0.7465
Epoch 1/2... Discriminator Loss: 1.4143... Generator Loss: 0.7660
Epoch 1/2... Discriminator Loss: 1.4100... Generator Loss: 0.7874
Epoch 1/2... Discriminator Loss: 1.4591... Generator Loss: 0.7544
Epoch 1/2... Discriminator Loss: 1.4264... Generator Loss: 0.6984
Epoch 1/2... Discriminator Loss: 1.4048... Generator Loss: 0.8407
Epoch 1/2... Discriminator Loss: 1.3971... Generator Loss: 0.7421
Epoch 1/2... Discriminator Loss: 1.4221... Generator Loss: 0.7217
Epoch 1/2... Discriminator Loss: 1.3976... Generator Loss: 0.7478
Epoch 1/2... Discriminator Loss: 1.4596... Generator Loss: 0.6913
Epoch 1/2... Discriminator Loss: 1.4063... Generator Loss: 0.7880
Epoch 1/2... Discriminator Loss: 1.4581... Generator Loss: 0.7134
Epoch 1/2... Discriminator Loss: 1.4258... Generator Loss: 0.7722
Epoch 1/2... Discriminator Loss: 1.4813... Generator Loss: 0.6909
Epoch 1/2... Discriminator Loss: 1.4322... Generator Loss: 0.7715
Epoch 1/2... Discriminator Loss: 1.4390... Generator Loss: 0.6980
Epoch 1/2... Discriminator Loss: 1.4304... Generator Loss: 0.6945
Epoch 1/2... Discriminator Loss: 1.4241... Generator Loss: 0.7230
Epoch 1/2... Discriminator Loss: 1.4320... Generator Loss: 0.7048
Epoch 1/2... Discriminator Loss: 1.3959... Generator Loss: 0.7921
Epoch 1/2... Discriminator Loss: 1.4455... Generator Loss: 0.6440
Epoch 1/2... Discriminator Loss: 1.4764... Generator Loss: 0.7165
Epoch 1/2... Discriminator Loss: 1.4144... Generator Loss: 0.7222
Epoch 1/2... Discriminator Loss: 1.4207... Generator Loss: 0.7562
Epoch 1/2... Discriminator Loss: 1.4534... Generator Loss: 0.6819
Epoch 1/2... Discriminator Loss: 1.4263... Generator Loss: 0.7434
Epoch 1/2... Discriminator Loss: 1.5204... Generator Loss: 0.6617
Epoch 1/2... Discriminator Loss: 1.4769... Generator Loss: 0.7155
Epoch 1/2... Discriminator Loss: 1.4152... Generator Loss: 0.7705
Epoch 1/2... Discriminator Loss: 1.4297... Generator Loss: 0.6776
Epoch 1/2... Discriminator Loss: 1.4193... Generator Loss: 0.6992
Epoch 1/2... Discriminator Loss: 1.3918... Generator Loss: 0.7316
Epoch 1/2... Discriminator Loss: 1.4733... Generator Loss: 0.7446
Epoch 1/2... Discriminator Loss: 1.4174... Generator Loss: 0.7802
Epoch 1/2... Discriminator Loss: 1.4234... Generator Loss: 0.7791
Epoch 1/2... Discriminator Loss: 1.4294... Generator Loss: 0.6782
Epoch 1/2... Discriminator Loss: 1.4025... Generator Loss: 0.7807
Epoch 1/2... Discriminator Loss: 1.4238... Generator Loss: 0.7098
Epoch 1/2... Discriminator Loss: 1.4611... Generator Loss: 0.6458
Epoch 1/2... Discriminator Loss: 1.4278... Generator Loss: 0.7725
Epoch 1/2... Discriminator Loss: 1.4327... Generator Loss: 0.7564
Epoch 1/2... Discriminator Loss: 1.4311... Generator Loss: 0.7037
Epoch 1/2... Discriminator Loss: 1.4088... Generator Loss: 0.8170
Epoch 1/2... Discriminator Loss: 1.4376... Generator Loss: 0.6972
Epoch 1/2... Discriminator Loss: 1.3901... Generator Loss: 0.8476
Epoch 1/2... Discriminator Loss: 1.4116... Generator Loss: 0.8314
Epoch 1/2... Discriminator Loss: 1.4420... Generator Loss: 0.7027
Epoch 1/2... Discriminator Loss: 1.4444... Generator Loss: 0.7647
Epoch 1/2... Discriminator Loss: 1.4145... Generator Loss: 0.7929
Epoch 1/2... Discriminator Loss: 1.4256... Generator Loss: 0.7951
Epoch 1/2... Discriminator Loss: 1.4470... Generator Loss: 0.6981
Epoch 1/2... Discriminator Loss: 1.3822... Generator Loss: 0.7528
Epoch 1/2... Discriminator Loss: 1.4204... Generator Loss: 0.7495
Epoch 1/2... Discriminator Loss: 1.4120... Generator Loss: 0.8066
Epoch 1/2... Discriminator Loss: 1.4040... Generator Loss: 0.7499
Epoch 1/2... Discriminator Loss: 1.4394... Generator Loss: 0.7879
Epoch 1/2... Discriminator Loss: 1.4037... Generator Loss: 0.8267
Epoch 1/2... Discriminator Loss: 1.4630... Generator Loss: 0.8065
Epoch 1/2... Discriminator Loss: 1.3997... Generator Loss: 0.7323
Epoch 1/2... Discriminator Loss: 1.4781... Generator Loss: 0.7486
Epoch 1/2... Discriminator Loss: 1.4497... Generator Loss: 0.7481
Epoch 1/2... Discriminator Loss: 1.4081... Generator Loss: 0.7549
Epoch 1/2... Discriminator Loss: 1.4444... Generator Loss: 0.6960
Epoch 1/2... Discriminator Loss: 1.4313... Generator Loss: 0.7281
Epoch 1/2... Discriminator Loss: 1.4155... Generator Loss: 0.8141
Epoch 1/2... Discriminator Loss: 1.4853... Generator Loss: 0.7802
Epoch 1/2... Discriminator Loss: 1.4020... Generator Loss: 0.7457
Epoch 1/2... Discriminator Loss: 1.4398... Generator Loss: 0.7547
Epoch 1/2... Discriminator Loss: 1.3986... Generator Loss: 0.7983
Epoch 1/2... Discriminator Loss: 1.4423... Generator Loss: 0.7480
Epoch 1/2... Discriminator Loss: 1.4649... Generator Loss: 0.7545
Epoch 1/2... Discriminator Loss: 1.4294... Generator Loss: 0.7444
Epoch 1/2... Discriminator Loss: 1.4286... Generator Loss: 0.7667
Epoch 1/2... Discriminator Loss: 1.3956... Generator Loss: 0.7613
Epoch 1/2... Discriminator Loss: 1.4116... Generator Loss: 0.7303
Epoch 1/2... Discriminator Loss: 1.4234... Generator Loss: 0.7737
Epoch 1/2... Discriminator Loss: 1.4486... Generator Loss: 0.7689
Epoch 1/2... Discriminator Loss: 1.4149... Generator Loss: 0.7175
Epoch 1/2... Discriminator Loss: 1.3770... Generator Loss: 0.7843
Epoch 1/2... Discriminator Loss: 1.4480... Generator Loss: 0.7239
Epoch 1/2... Discriminator Loss: 1.3876... Generator Loss: 0.7799
Epoch 1/2... Discriminator Loss: 1.3978... Generator Loss: 0.6859
Epoch 1/2... Discriminator Loss: 1.4411... Generator Loss: 0.8208
Epoch 1/2... Discriminator Loss: 1.4302... Generator Loss: 0.8339
Epoch 1/2... Discriminator Loss: 1.4061... Generator Loss: 0.8117
Epoch 1/2... Discriminator Loss: 1.4246... Generator Loss: 0.7776
Epoch 1/2... Discriminator Loss: 1.4060... Generator Loss: 0.7884
Epoch 1/2... Discriminator Loss: 1.4194... Generator Loss: 0.7383
Epoch 1/2... Discriminator Loss: 1.3893... Generator Loss: 0.7631
Epoch 1/2... Discriminator Loss: 1.3948... Generator Loss: 0.7870
Epoch 1/2... Discriminator Loss: 1.4048... Generator Loss: 0.7395
Epoch 1/2... Discriminator Loss: 1.3897... Generator Loss: 0.8285
Epoch 1/2... Discriminator Loss: 1.4151... Generator Loss: 0.8459
Epoch 1/2... Discriminator Loss: 1.4259... Generator Loss: 0.7427
Epoch 1/2... Discriminator Loss: 1.4251... Generator Loss: 0.8115
Epoch 1/2... Discriminator Loss: 1.3774... Generator Loss: 0.7847
Epoch 1/2... Discriminator Loss: 1.4516... Generator Loss: 0.8219
Epoch 1/2... Discriminator Loss: 1.4198... Generator Loss: 0.8052
Epoch 1/2... Discriminator Loss: 1.4095... Generator Loss: 0.6971
Epoch 1/2... Discriminator Loss: 1.4028... Generator Loss: 0.7597
Epoch 1/2... Discriminator Loss: 1.4053... Generator Loss: 0.7517
Epoch 1/2... Discriminator Loss: 1.5479... Generator Loss: 0.5905
Epoch 1/2... Discriminator Loss: 1.3958... Generator Loss: 0.6834
Epoch 1/2... Discriminator Loss: 1.4701... Generator Loss: 0.6608
Epoch 1/2... Discriminator Loss: 1.3940... Generator Loss: 0.7775
Epoch 1/2... Discriminator Loss: 1.3790... Generator Loss: 0.8132
Epoch 1/2... Discriminator Loss: 1.3895... Generator Loss: 0.8154
Epoch 1/2... Discriminator Loss: 1.4180... Generator Loss: 0.7714
Epoch 1/2... Discriminator Loss: 1.4009... Generator Loss: 0.7872
Epoch 1/2... Discriminator Loss: 1.4255... Generator Loss: 0.7022
Epoch 1/2... Discriminator Loss: 1.4344... Generator Loss: 0.7885
Epoch 1/2... Discriminator Loss: 1.4147... Generator Loss: 0.7337
Epoch 1/2... Discriminator Loss: 1.3886... Generator Loss: 0.6931
Epoch 1/2... Discriminator Loss: 1.4255... Generator Loss: 0.6784
Epoch 1/2... Discriminator Loss: 1.3924... Generator Loss: 0.8169
Epoch 1/2... Discriminator Loss: 1.4084... Generator Loss: 0.7266
Epoch 1/2... Discriminator Loss: 1.4304... Generator Loss: 0.7018
Epoch 1/2... Discriminator Loss: 1.4075... Generator Loss: 0.8292
Epoch 1/2... Discriminator Loss: 1.3897... Generator Loss: 0.7375
Epoch 1/2... Discriminator Loss: 1.3832... Generator Loss: 0.8089
Epoch 1/2... Discriminator Loss: 1.4234... Generator Loss: 0.7355
Epoch 1/2... Discriminator Loss: 1.3707... Generator Loss: 0.8393
Epoch 1/2... Discriminator Loss: 1.3807... Generator Loss: 0.7932
Epoch 1/2... Discriminator Loss: 1.3870... Generator Loss: 0.6892
Epoch 1/2... Discriminator Loss: 1.4376... Generator Loss: 0.7632
Epoch 1/2... Discriminator Loss: 1.3973... Generator Loss: 0.7970
Epoch 1/2... Discriminator Loss: 1.3908... Generator Loss: 0.7474
Epoch 1/2... Discriminator Loss: 1.3964... Generator Loss: 0.7694
Epoch 1/2... Discriminator Loss: 1.4129... Generator Loss: 0.6868
Epoch 1/2... Discriminator Loss: 1.4195... Generator Loss: 0.7275
Epoch 1/2... Discriminator Loss: 1.4203... Generator Loss: 0.7108
Epoch 1/2... Discriminator Loss: 1.3780... Generator Loss: 0.7789
Epoch 1/2... Discriminator Loss: 1.3715... Generator Loss: 0.8001
Epoch 1/2... Discriminator Loss: 1.4150... Generator Loss: 0.7274
Epoch 1/2... Discriminator Loss: 1.4436... Generator Loss: 0.7984
Epoch 1/2... Discriminator Loss: 1.3829... Generator Loss: 0.7845
Epoch 1/2... Discriminator Loss: 1.3992... Generator Loss: 0.7718
Epoch 1/2... Discriminator Loss: 1.4340... Generator Loss: 0.7867
Epoch 1/2... Discriminator Loss: 1.4104... Generator Loss: 0.7830
Epoch 1/2... Discriminator Loss: 1.4086... Generator Loss: 0.8699
Epoch 1/2... Discriminator Loss: 1.3961... Generator Loss: 0.8850
Epoch 1/2... Discriminator Loss: 1.4179... Generator Loss: 0.7481
Epoch 1/2... Discriminator Loss: 1.4073... Generator Loss: 0.7061
Epoch 1/2... Discriminator Loss: 1.4046... Generator Loss: 0.7060
Epoch 1/2... Discriminator Loss: 1.4103... Generator Loss: 0.6961
Epoch 1/2... Discriminator Loss: 1.4126... Generator Loss: 0.7398
Epoch 1/2... Discriminator Loss: 1.3805... Generator Loss: 0.8365
Epoch 1/2... Discriminator Loss: 1.3757... Generator Loss: 0.8014
Epoch 1/2... Discriminator Loss: 1.3994... Generator Loss: 0.7145
Epoch 1/2... Discriminator Loss: 1.4017... Generator Loss: 0.7445
Epoch 1/2... Discriminator Loss: 1.3972... Generator Loss: 0.7402
Epoch 1/2... Discriminator Loss: 1.3899... Generator Loss: 0.7881
Epoch 2/2... Discriminator Loss: 1.4101... Generator Loss: 0.7739
Epoch 2/2... Discriminator Loss: 1.4317... Generator Loss: 0.8301
Epoch 2/2... Discriminator Loss: 1.4036... Generator Loss: 0.7974
Epoch 2/2... Discriminator Loss: 1.3901... Generator Loss: 0.7652
Epoch 2/2... Discriminator Loss: 1.4027... Generator Loss: 0.7790
Epoch 2/2... Discriminator Loss: 1.4244... Generator Loss: 0.7627
Epoch 2/2... Discriminator Loss: 1.4102... Generator Loss: 0.7264
Epoch 2/2... Discriminator Loss: 1.4065... Generator Loss: 0.8078
Epoch 2/2... Discriminator Loss: 1.3743... Generator Loss: 0.8560
Epoch 2/2... Discriminator Loss: 1.4053... Generator Loss: 0.7110
Epoch 2/2... Discriminator Loss: 1.3894... Generator Loss: 0.7446
Epoch 2/2... Discriminator Loss: 1.3801... Generator Loss: 0.7851
Epoch 2/2... Discriminator Loss: 1.4050... Generator Loss: 0.7603
Epoch 2/2... Discriminator Loss: 1.4056... Generator Loss: 0.7680
Epoch 2/2... Discriminator Loss: 1.3851... Generator Loss: 0.8226
Epoch 2/2... Discriminator Loss: 1.3887... Generator Loss: 0.8719
Epoch 2/2... Discriminator Loss: 1.3853... Generator Loss: 0.7933
Epoch 2/2... Discriminator Loss: 1.3882... Generator Loss: 0.8353
Epoch 2/2... Discriminator Loss: 1.4187... Generator Loss: 0.7735
Epoch 2/2... Discriminator Loss: 1.4005... Generator Loss: 0.8329
Epoch 2/2... Discriminator Loss: 1.4035... Generator Loss: 0.7444
Epoch 2/2... Discriminator Loss: 1.4358... Generator Loss: 0.8471
Epoch 2/2... Discriminator Loss: 1.3955... Generator Loss: 0.7766
Epoch 2/2... Discriminator Loss: 1.4064... Generator Loss: 0.7166
Epoch 2/2... Discriminator Loss: 1.4073... Generator Loss: 0.7650
Epoch 2/2... Discriminator Loss: 1.3972... Generator Loss: 0.8111
Epoch 2/2... Discriminator Loss: 1.4196... Generator Loss: 0.7401
Epoch 2/2... Discriminator Loss: 1.4041... Generator Loss: 0.6905
Epoch 2/2... Discriminator Loss: 1.3673... Generator Loss: 0.8579
Epoch 2/2... Discriminator Loss: 1.4101... Generator Loss: 0.7957
Epoch 2/2... Discriminator Loss: 1.3692... Generator Loss: 0.7731
Epoch 2/2... Discriminator Loss: 1.4084... Generator Loss: 0.6944
Epoch 2/2... Discriminator Loss: 1.4021... Generator Loss: 0.7616
Epoch 2/2... Discriminator Loss: 1.4181... Generator Loss: 0.7844
Epoch 2/2... Discriminator Loss: 1.3941... Generator Loss: 0.7254
Epoch 2/2... Discriminator Loss: 1.3899... Generator Loss: 0.7818
Epoch 2/2... Discriminator Loss: 1.3786... Generator Loss: 0.8802
Epoch 2/2... Discriminator Loss: 1.4032... Generator Loss: 0.8099
Epoch 2/2... Discriminator Loss: 1.3941... Generator Loss: 0.7672
Epoch 2/2... Discriminator Loss: 1.4107... Generator Loss: 0.8151
Epoch 2/2... Discriminator Loss: 1.4739... Generator Loss: 0.6283
Epoch 2/2... Discriminator Loss: 1.4033... Generator Loss: 0.8148
Epoch 2/2... Discriminator Loss: 1.4521... Generator Loss: 0.6761
Epoch 2/2... Discriminator Loss: 1.4129... Generator Loss: 0.7839
Epoch 2/2... Discriminator Loss: 1.3901... Generator Loss: 0.9085
Epoch 2/2... Discriminator Loss: 1.4239... Generator Loss: 0.7720
Epoch 2/2... Discriminator Loss: 1.4284... Generator Loss: 0.7361
Epoch 2/2... Discriminator Loss: 1.4068... Generator Loss: 0.7519
Epoch 2/2... Discriminator Loss: 1.3935... Generator Loss: 0.7754
Epoch 2/2... Discriminator Loss: 1.4097... Generator Loss: 0.8206
Epoch 2/2... Discriminator Loss: 1.3770... Generator Loss: 0.7165
Epoch 2/2... Discriminator Loss: 1.4208... Generator Loss: 0.8539
Epoch 2/2... Discriminator Loss: 1.3913... Generator Loss: 0.7809
Epoch 2/2... Discriminator Loss: 1.4223... Generator Loss: 0.6754
Epoch 2/2... Discriminator Loss: 1.4056... Generator Loss: 0.7737
Epoch 2/2... Discriminator Loss: 1.3853... Generator Loss: 0.8308
Epoch 2/2... Discriminator Loss: 1.3892... Generator Loss: 0.7550
Epoch 2/2... Discriminator Loss: 1.3775... Generator Loss: 0.7582
Epoch 2/2... Discriminator Loss: 1.4376... Generator Loss: 0.7921
Epoch 2/2... Discriminator Loss: 1.3987... Generator Loss: 0.7437
Epoch 2/2... Discriminator Loss: 1.3874... Generator Loss: 0.8198
Epoch 2/2... Discriminator Loss: 1.3991... Generator Loss: 0.7582
Epoch 2/2... Discriminator Loss: 1.3926... Generator Loss: 0.8019
Epoch 2/2... Discriminator Loss: 1.3979... Generator Loss: 0.7996
Epoch 2/2... Discriminator Loss: 1.4079... Generator Loss: 0.7462
Epoch 2/2... Discriminator Loss: 1.4059... Generator Loss: 0.7695
Epoch 2/2... Discriminator Loss: 1.3914... Generator Loss: 0.8214
Epoch 2/2... Discriminator Loss: 1.3969... Generator Loss: 0.7642
Epoch 2/2... Discriminator Loss: 1.3804... Generator Loss: 0.7781
Epoch 2/2... Discriminator Loss: 1.4184... Generator Loss: 0.6936
Epoch 2/2... Discriminator Loss: 1.4120... Generator Loss: 0.7292
Epoch 2/2... Discriminator Loss: 1.3816... Generator Loss: 0.8198
Epoch 2/2... Discriminator Loss: 1.3899... Generator Loss: 0.8159
Epoch 2/2... Discriminator Loss: 1.3811... Generator Loss: 0.8238
Epoch 2/2... Discriminator Loss: 1.3935... Generator Loss: 0.8247
Epoch 2/2... Discriminator Loss: 1.3841... Generator Loss: 0.7100
Epoch 2/2... Discriminator Loss: 1.3846... Generator Loss: 0.7984
Epoch 2/2... Discriminator Loss: 1.3896... Generator Loss: 0.7417
Epoch 2/2... Discriminator Loss: 1.4139... Generator Loss: 0.7813
Epoch 2/2... Discriminator Loss: 1.4088... Generator Loss: 0.7905
Epoch 2/2... Discriminator Loss: 1.3896... Generator Loss: 0.7310
Epoch 2/2... Discriminator Loss: 1.3979... Generator Loss: 0.7582
Epoch 2/2... Discriminator Loss: 1.4214... Generator Loss: 0.7060
Epoch 2/2... Discriminator Loss: 1.3702... Generator Loss: 0.7833
Epoch 2/2... Discriminator Loss: 1.4088... Generator Loss: 0.7707
Epoch 2/2... Discriminator Loss: 1.4260... Generator Loss: 0.7274
Epoch 2/2... Discriminator Loss: 1.3947... Generator Loss: 0.7646
Epoch 2/2... Discriminator Loss: 1.4158... Generator Loss: 0.7306
Epoch 2/2... Discriminator Loss: 1.4214... Generator Loss: 0.7681
Epoch 2/2... Discriminator Loss: 1.4091... Generator Loss: 0.8249
Epoch 2/2... Discriminator Loss: 1.3875... Generator Loss: 0.7884
Epoch 2/2... Discriminator Loss: 1.3822... Generator Loss: 0.7735
Epoch 2/2... Discriminator Loss: 1.3928... Generator Loss: 0.7849
Epoch 2/2... Discriminator Loss: 1.4221... Generator Loss: 0.6927
Epoch 2/2... Discriminator Loss: 1.4032... Generator Loss: 0.8080
Epoch 2/2... Discriminator Loss: 1.3662... Generator Loss: 0.7602
Epoch 2/2... Discriminator Loss: 1.4087... Generator Loss: 0.7672
Epoch 2/2... Discriminator Loss: 1.4230... Generator Loss: 0.7303
Epoch 2/2... Discriminator Loss: 1.4216... Generator Loss: 0.7869
Epoch 2/2... Discriminator Loss: 1.3778... Generator Loss: 0.7638
Epoch 2/2... Discriminator Loss: 1.3774... Generator Loss: 0.8564
Epoch 2/2... Discriminator Loss: 1.3761... Generator Loss: 0.8062
Epoch 2/2... Discriminator Loss: 1.3865... Generator Loss: 0.7908
Epoch 2/2... Discriminator Loss: 1.3661... Generator Loss: 0.7723
Epoch 2/2... Discriminator Loss: 1.4114... Generator Loss: 0.8230
Epoch 2/2... Discriminator Loss: 1.4052... Generator Loss: 0.7799
Epoch 2/2... Discriminator Loss: 1.3974... Generator Loss: 0.7660
Epoch 2/2... Discriminator Loss: 1.4102... Generator Loss: 0.7512
Epoch 2/2... Discriminator Loss: 1.3921... Generator Loss: 0.7801
Epoch 2/2... Discriminator Loss: 1.4100... Generator Loss: 0.8291
Epoch 2/2... Discriminator Loss: 1.4003... Generator Loss: 0.7990
Epoch 2/2... Discriminator Loss: 1.4205... Generator Loss: 0.6965
Epoch 2/2... Discriminator Loss: 1.3931... Generator Loss: 0.8034
Epoch 2/2... Discriminator Loss: 1.4131... Generator Loss: 0.6918
Epoch 2/2... Discriminator Loss: 1.4213... Generator Loss: 0.7696
Epoch 2/2... Discriminator Loss: 1.3762... Generator Loss: 0.8294
Epoch 2/2... Discriminator Loss: 1.4225... Generator Loss: 0.7199
Epoch 2/2... Discriminator Loss: 1.3901... Generator Loss: 0.7668
Epoch 2/2... Discriminator Loss: 1.3980... Generator Loss: 0.7547
Epoch 2/2... Discriminator Loss: 1.4104... Generator Loss: 0.7675
Epoch 2/2... Discriminator Loss: 1.3882... Generator Loss: 0.7776
Epoch 2/2... Discriminator Loss: 1.3892... Generator Loss: 0.8144
Epoch 2/2... Discriminator Loss: 1.4016... Generator Loss: 0.7682
Epoch 2/2... Discriminator Loss: 1.3874... Generator Loss: 0.7568
Epoch 2/2... Discriminator Loss: 1.4094... Generator Loss: 0.7134
Epoch 2/2... Discriminator Loss: 1.4111... Generator Loss: 0.7983
Epoch 2/2... Discriminator Loss: 1.3856... Generator Loss: 0.8807
Epoch 2/2... Discriminator Loss: 1.4057... Generator Loss: 0.7136
Epoch 2/2... Discriminator Loss: 1.3907... Generator Loss: 0.7715
Epoch 2/2... Discriminator Loss: 1.4017... Generator Loss: 0.8213
Epoch 2/2... Discriminator Loss: 1.3965... Generator Loss: 0.7884
Epoch 2/2... Discriminator Loss: 1.3973... Generator Loss: 0.8294
Epoch 2/2... Discriminator Loss: 1.3920... Generator Loss: 0.8063
Epoch 2/2... Discriminator Loss: 1.3815... Generator Loss: 0.7426
Epoch 2/2... Discriminator Loss: 1.3790... Generator Loss: 0.7587
Epoch 2/2... Discriminator Loss: 1.4055... Generator Loss: 0.7462
Epoch 2/2... Discriminator Loss: 1.3927... Generator Loss: 0.7395
Epoch 2/2... Discriminator Loss: 1.3801... Generator Loss: 0.7457
Epoch 2/2... Discriminator Loss: 1.3868... Generator Loss: 0.8010
Epoch 2/2... Discriminator Loss: 1.3503... Generator Loss: 0.7850
Epoch 2/2... Discriminator Loss: 1.3787... Generator Loss: 0.7516
Epoch 2/2... Discriminator Loss: 1.3911... Generator Loss: 0.7643
Epoch 2/2... Discriminator Loss: 1.3519... Generator Loss: 0.8481
Epoch 2/2... Discriminator Loss: 1.3750... Generator Loss: 0.8142
Epoch 2/2... Discriminator Loss: 1.3888... Generator Loss: 0.7684
Epoch 2/2... Discriminator Loss: 1.3960... Generator Loss: 0.7915
Epoch 2/2... Discriminator Loss: 1.3943... Generator Loss: 0.7894
Epoch 2/2... Discriminator Loss: 1.3897... Generator Loss: 0.7740
Epoch 2/2... Discriminator Loss: 1.3750... Generator Loss: 0.8066
Epoch 2/2... Discriminator Loss: 1.3962... Generator Loss: 0.7593
Epoch 2/2... Discriminator Loss: 1.3863... Generator Loss: 0.7869
Epoch 2/2... Discriminator Loss: 1.3983... Generator Loss: 0.8116
Epoch 2/2... Discriminator Loss: 1.4043... Generator Loss: 0.8085
Epoch 2/2... Discriminator Loss: 1.3874... Generator Loss: 0.7529
Epoch 2/2... Discriminator Loss: 1.4218... Generator Loss: 0.7840
Epoch 2/2... Discriminator Loss: 1.3844... Generator Loss: 0.8197
Epoch 2/2... Discriminator Loss: 1.3822... Generator Loss: 0.7719
Epoch 2/2... Discriminator Loss: 1.3974... Generator Loss: 0.7405
Epoch 2/2... Discriminator Loss: 1.3877... Generator Loss: 0.7513
Epoch 2/2... Discriminator Loss: 1.3730... Generator Loss: 0.7440
Epoch 2/2... Discriminator Loss: 1.3932... Generator Loss: 0.7193
Epoch 2/2... Discriminator Loss: 1.3683... Generator Loss: 0.8044
Epoch 2/2... Discriminator Loss: 1.3395... Generator Loss: 0.8268
Epoch 2/2... Discriminator Loss: 1.3800... Generator Loss: 0.8551
Epoch 2/2... Discriminator Loss: 1.3994... Generator Loss: 0.7828
Epoch 2/2... Discriminator Loss: 1.4170... Generator Loss: 0.7637
Epoch 2/2... Discriminator Loss: 1.4201... Generator Loss: 0.7366
Epoch 2/2... Discriminator Loss: 1.3769... Generator Loss: 0.7664
Epoch 2/2... Discriminator Loss: 1.3937... Generator Loss: 0.8009
Epoch 2/2... Discriminator Loss: 1.3735... Generator Loss: 0.7685
Epoch 2/2... Discriminator Loss: 1.3608... Generator Loss: 0.7752
Epoch 2/2... Discriminator Loss: 1.3953... Generator Loss: 0.7192
Epoch 2/2... Discriminator Loss: 1.3906... Generator Loss: 0.7283
Epoch 2/2... Discriminator Loss: 1.3998... Generator Loss: 0.7233
Epoch 2/2... Discriminator Loss: 1.3956... Generator Loss: 0.8407
Epoch 2/2... Discriminator Loss: 1.3768... Generator Loss: 0.7911
Epoch 2/2... Discriminator Loss: 1.3837... Generator Loss: 0.8278
Epoch 2/2... Discriminator Loss: 1.4051... Generator Loss: 0.7857
Epoch 2/2... Discriminator Loss: 1.3639... Generator Loss: 0.8149
Epoch 2/2... Discriminator Loss: 1.3694... Generator Loss: 0.8149
Epoch 2/2... Discriminator Loss: 1.4090... Generator Loss: 0.7594
Epoch 2/2... Discriminator Loss: 1.3840... Generator Loss: 0.7826
Epoch 2/2... Discriminator Loss: 1.3905... Generator Loss: 0.7920
Epoch 2/2... Discriminator Loss: 1.3858... Generator Loss: 0.8497
Epoch 2/2... Discriminator Loss: 1.4037... Generator Loss: 0.7700
Epoch 2/2... Discriminator Loss: 1.3954... Generator Loss: 0.7942
Epoch 2/2... Discriminator Loss: 1.3828... Generator Loss: 0.8583
Epoch 2/2... Discriminator Loss: 1.3898... Generator Loss: 0.7996
Epoch 2/2... Discriminator Loss: 1.3866... Generator Loss: 0.7538
Epoch 2/2... Discriminator Loss: 1.4044... Generator Loss: 0.8415
Epoch 2/2... Discriminator Loss: 1.3740... Generator Loss: 0.8226
Epoch 2/2... Discriminator Loss: 1.3800... Generator Loss: 0.8113
Epoch 2/2... Discriminator Loss: 1.3766... Generator Loss: 0.8041
Epoch 2/2... Discriminator Loss: 1.3946... Generator Loss: 0.7750
Epoch 2/2... Discriminator Loss: 1.3848... Generator Loss: 0.8150
Epoch 2/2... Discriminator Loss: 1.3992... Generator Loss: 0.6908
Epoch 2/2... Discriminator Loss: 1.3789... Generator Loss: 0.7594
Epoch 2/2... Discriminator Loss: 1.3654... Generator Loss: 0.7778
Epoch 2/2... Discriminator Loss: 1.3792... Generator Loss: 0.7156
Epoch 2/2... Discriminator Loss: 1.3741... Generator Loss: 0.7768
Epoch 2/2... Discriminator Loss: 1.3901... Generator Loss: 0.7996
Epoch 2/2... Discriminator Loss: 1.3891... Generator Loss: 0.7605
Epoch 2/2... Discriminator Loss: 1.3827... Generator Loss: 0.7825
Epoch 2/2... Discriminator Loss: 1.3755... Generator Loss: 0.8548
Epoch 2/2... Discriminator Loss: 1.3827... Generator Loss: 0.7292
Epoch 2/2... Discriminator Loss: 1.3993... Generator Loss: 0.7333
Epoch 2/2... Discriminator Loss: 1.3727... Generator Loss: 0.7879
Epoch 2/2... Discriminator Loss: 1.4019... Generator Loss: 0.6984
Epoch 2/2... Discriminator Loss: 1.3765... Generator Loss: 0.7946
Epoch 2/2... Discriminator Loss: 1.3776... Generator Loss: 0.7877
Epoch 2/2... Discriminator Loss: 1.4013... Generator Loss: 0.7333
Epoch 2/2... Discriminator Loss: 1.4021... Generator Loss: 0.7381
Epoch 2/2... Discriminator Loss: 1.3779... Generator Loss: 0.7772
Epoch 2/2... Discriminator Loss: 1.3997... Generator Loss: 0.8015
Epoch 2/2... Discriminator Loss: 1.3545... Generator Loss: 0.7616
Epoch 2/2... Discriminator Loss: 1.3730... Generator Loss: 0.8021
Epoch 2/2... Discriminator Loss: 1.3624... Generator Loss: 0.7690
Epoch 2/2... Discriminator Loss: 1.3979... Generator Loss: 0.9400
Epoch 2/2... Discriminator Loss: 1.3883... Generator Loss: 0.8104
Epoch 2/2... Discriminator Loss: 1.3955... Generator Loss: 0.7885
Epoch 2/2... Discriminator Loss: 1.3594... Generator Loss: 0.8803
Epoch 2/2... Discriminator Loss: 1.3690... Generator Loss: 0.7475
Epoch 2/2... Discriminator Loss: 1.3700... Generator Loss: 0.7542
Epoch 2/2... Discriminator Loss: 1.4003... Generator Loss: 0.7682
Epoch 2/2... Discriminator Loss: 1.3793... Generator Loss: 0.7422
Epoch 2/2... Discriminator Loss: 1.3905... Generator Loss: 0.7485
Epoch 2/2... Discriminator Loss: 1.3744... Generator Loss: 0.8238
Epoch 2/2... Discriminator Loss: 1.3859... Generator Loss: 0.7162
Epoch 2/2... Discriminator Loss: 1.3888... Generator Loss: 0.7824
Epoch 2/2... Discriminator Loss: 1.4000... Generator Loss: 0.8110
Epoch 2/2... Discriminator Loss: 1.3533... Generator Loss: 0.8884
Epoch 2/2... Discriminator Loss: 1.3961... Generator Loss: 0.7791
Epoch 2/2... Discriminator Loss: 1.4023... Generator Loss: 0.7703
Epoch 2/2... Discriminator Loss: 1.3962... Generator Loss: 0.7437
Epoch 2/2... Discriminator Loss: 1.3795... Generator Loss: 0.7922
Epoch 2/2... Discriminator Loss: 1.3836... Generator Loss: 0.7789
Epoch 2/2... Discriminator Loss: 1.3817... Generator Loss: 0.7576
Epoch 2/2... Discriminator Loss: 1.3979... Generator Loss: 0.7982
Epoch 2/2... Discriminator Loss: 1.3788... Generator Loss: 0.8280
Epoch 2/2... Discriminator Loss: 1.3727... Generator Loss: 0.8106
Epoch 2/2... Discriminator Loss: 1.3713... Generator Loss: 0.7791
Epoch 2/2... Discriminator Loss: 1.3799... Generator Loss: 0.8300
Epoch 2/2... Discriminator Loss: 1.3725... Generator Loss: 0.7826
Epoch 2/2... Discriminator Loss: 1.3760... Generator Loss: 0.8687
Epoch 2/2... Discriminator Loss: 1.4068... Generator Loss: 0.7777
Epoch 2/2... Discriminator Loss: 1.4083... Generator Loss: 0.7837
Epoch 2/2... Discriminator Loss: 1.4109... Generator Loss: 0.7467
Epoch 2/2... Discriminator Loss: 1.4075... Generator Loss: 0.7314
Epoch 2/2... Discriminator Loss: 1.3976... Generator Loss: 0.8516
Epoch 2/2... Discriminator Loss: 1.4040... Generator Loss: 0.7233
Epoch 2/2... Discriminator Loss: 1.3664... Generator Loss: 0.7737
Epoch 2/2... Discriminator Loss: 1.3877... Generator Loss: 0.8272
Epoch 2/2... Discriminator Loss: 1.3880... Generator Loss: 0.7265
Epoch 2/2... Discriminator Loss: 1.4177... Generator Loss: 0.8458
Epoch 2/2... Discriminator Loss: 1.3855... Generator Loss: 0.8041
Epoch 2/2... Discriminator Loss: 1.3825... Generator Loss: 0.8100
Epoch 2/2... Discriminator Loss: 1.4032... Generator Loss: 0.9776
Epoch 2/2... Discriminator Loss: 1.3860... Generator Loss: 0.8336
Epoch 2/2... Discriminator Loss: 1.4224... Generator Loss: 0.7505
Epoch 2/2... Discriminator Loss: 1.3420... Generator Loss: 0.7955
Epoch 2/2... Discriminator Loss: 1.3991... Generator Loss: 0.7629
Epoch 2/2... Discriminator Loss: 1.3667... Generator Loss: 0.8093
Epoch 2/2... Discriminator Loss: 1.3831... Generator Loss: 0.8362
Epoch 2/2... Discriminator Loss: 1.3820... Generator Loss: 0.8432
Epoch 2/2... Discriminator Loss: 1.3851... Generator Loss: 0.8058
Epoch 2/2... Discriminator Loss: 1.3740... Generator Loss: 0.8083
Epoch 2/2... Discriminator Loss: 1.4217... Generator Loss: 0.6944
Epoch 2/2... Discriminator Loss: 1.3808... Generator Loss: 0.7524
Epoch 2/2... Discriminator Loss: 1.3823... Generator Loss: 0.7830
Epoch 2/2... Discriminator Loss: 1.3961... Generator Loss: 0.8007
Epoch 2/2... Discriminator Loss: 1.3865... Generator Loss: 0.8170
Epoch 2/2... Discriminator Loss: 1.3796... Generator Loss: 0.8435
Epoch 2/2... Discriminator Loss: 1.3744... Generator Loss: 0.7954
Epoch 2/2... Discriminator Loss: 1.3805... Generator Loss: 0.8140
Epoch 2/2... Discriminator Loss: 1.3734... Generator Loss: 0.8915
Epoch 2/2... Discriminator Loss: 1.3934... Generator Loss: 0.7509
Epoch 2/2... Discriminator Loss: 1.3785... Generator Loss: 0.7673
Epoch 2/2... Discriminator Loss: 1.4004... Generator Loss: 0.8831
Epoch 2/2... Discriminator Loss: 1.3845... Generator Loss: 0.7382
Epoch 2/2... Discriminator Loss: 1.3597... Generator Loss: 0.8006
Epoch 2/2... Discriminator Loss: 1.4019... Generator Loss: 0.7255
Epoch 2/2... Discriminator Loss: 1.4012... Generator Loss: 0.7813
Epoch 2/2... Discriminator Loss: 1.3497... Generator Loss: 0.8318
Epoch 2/2... Discriminator Loss: 1.3984... Generator Loss: 0.7283
Epoch 2/2... Discriminator Loss: 1.3861... Generator Loss: 0.8141
Epoch 2/2... Discriminator Loss: 1.4044... Generator Loss: 0.7566
Epoch 2/2... Discriminator Loss: 1.3624... Generator Loss: 0.8573
Epoch 2/2... Discriminator Loss: 1.3974... Generator Loss: 0.7673
Epoch 2/2... Discriminator Loss: 1.3778... Generator Loss: 0.8487
Epoch 2/2... Discriminator Loss: 1.4078... Generator Loss: 0.6847
Epoch 2/2... Discriminator Loss: 1.3845... Generator Loss: 0.7582
Epoch 2/2... Discriminator Loss: 1.3898... Generator Loss: 0.8265
Epoch 2/2... Discriminator Loss: 1.3963... Generator Loss: 0.7567
Epoch 2/2... Discriminator Loss: 1.4102... Generator Loss: 0.6288
Epoch 2/2... Discriminator Loss: 1.3829... Generator Loss: 0.8011
Epoch 2/2... Discriminator Loss: 1.3772... Generator Loss: 0.7561
Epoch 2/2... Discriminator Loss: 1.4131... Generator Loss: 0.6988
Epoch 2/2... Discriminator Loss: 1.3885... Generator Loss: 0.7152
Epoch 2/2... Discriminator Loss: 1.3920... Generator Loss: 0.7587
Epoch 2/2... Discriminator Loss: 1.3744... Generator Loss: 0.8240
Epoch 2/2... Discriminator Loss: 1.3814... Generator Loss: 0.8052
Epoch 2/2... Discriminator Loss: 1.3729... Generator Loss: 0.8013
Epoch 2/2... Discriminator Loss: 1.3639... Generator Loss: 0.7400
Epoch 2/2... Discriminator Loss: 1.3889... Generator Loss: 0.7993
Epoch 2/2... Discriminator Loss: 1.3534... Generator Loss: 0.8126
Epoch 2/2... Discriminator Loss: 1.3650... Generator Loss: 0.7938
Epoch 2/2... Discriminator Loss: 1.3823... Generator Loss: 0.7935
Epoch 2/2... Discriminator Loss: 1.4126... Generator Loss: 0.6913
Epoch 2/2... Discriminator Loss: 1.3855... Generator Loss: 0.8330
Epoch 2/2... Discriminator Loss: 1.3759... Generator Loss: 0.8259
Epoch 2/2... Discriminator Loss: 1.3624... Generator Loss: 0.7561
Epoch 2/2... Discriminator Loss: 1.3752... Generator Loss: 0.7663
Epoch 2/2... Discriminator Loss: 1.3741... Generator Loss: 0.8071
Epoch 2/2... Discriminator Loss: 1.3807... Generator Loss: 0.8758
Epoch 2/2... Discriminator Loss: 1.3863... Generator Loss: 0.8169
Epoch 2/2... Discriminator Loss: 1.3709... Generator Loss: 0.8390
Epoch 2/2... Discriminator Loss: 1.3820... Generator Loss: 0.7698

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.

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